In this chapter, we introduced the central limit theorem before we covered normal distribution. Normal distribution is a smooth, strongly peaked function where the area under the curve is 1. We discussed how the normal distribution works as an excellent kernel for the kernel density estimator. We performed the kernel density estimation on a small dataset and discussed how shape of the data looked. We then performed kernel density estimation for the Monet price dataset and found the probability of a painting going for 5 million dollars or more. Our next chapter is going to be a section review, where we accumulate all of the content that we've gone over in this book so far.